






"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import GLPNImageProcessor






class 	a		(   unittest.TestCase						):






    def __init__(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				str=7			,		__SCREAMING_SNAKE_CASE	:				List[Any]=3			,		__SCREAMING_SNAKE_CASE	:				List[Any]=18			,		__SCREAMING_SNAKE_CASE	:				List[str]=30			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=400			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=True			,		__SCREAMING_SNAKE_CASE	:				Tuple=32			,		__SCREAMING_SNAKE_CASE	:				Dict=True			,		)	->						Optional[int]:
      lowerCamelCase_       	=		parent
      lowerCamelCase_       	=		batch_size
      lowerCamelCase_       	=		num_channels
      lowerCamelCase_       	=		image_size
      lowerCamelCase_       	=		min_resolution
      lowerCamelCase_       	=		max_resolution
      lowerCamelCase_       	=		do_resize
      lowerCamelCase_       	=		size_divisor
      lowerCamelCase_       	=		do_rescale






    def   UpperCamelCase			(			self	:				Optional[int]   )	->						List[str]:
      return {
          "do_resize": self.do_resize,
          "size_divisor": self.size_divisor,
          "do_rescale": self.do_rescale,
      }






@require_torch
@require_vision
class 	a		(   __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Dict		=					GLPNImageProcessor if is_vision_available() else None






    def   UpperCamelCase			(			self	:				Dict   )	->						str:
      lowerCamelCase_       	=		GLPNImageProcessingTester(self   )






    @property
    def   UpperCamelCase			(			self	:				Any   )	->						Optional[int]:
      return self.image_processor_tester.prepare_image_processor_dict()






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Any:
      lowerCamelCase_       	=		self.image_processing_class(**self.image_processor_dict   )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		'do_resize'   )   )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		'size_divisor'   )   )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		'resample'   )   )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		'do_rescale'   )   )






    def   UpperCamelCase			(			self	:				List[Any]   )	->						Optional[Any]:
      pass






    def   UpperCamelCase			(			self	:				int   )	->						Union[str, Any]:
      # Initialize image_processing
      lowerCamelCase_       	=		self.image_processing_class(**self.image_processor_dict   )
      # create random PIL images
      lowerCamelCase_       	=		prepare_image_inputs(self.image_processor_tester			,		equal_resolution=__SCREAMING_SNAKE_CASE   )
      for image in image_inputs:
        self.assertIsInstance(__SCREAMING_SNAKE_CASE			,		Image.Image   )

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      lowerCamelCase_       	=		image_processing(image_inputs[0]			,		return_tensors='pt'   ).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )






    def   UpperCamelCase			(			self	:				int   )	->						Tuple:
      # Initialize image_processing
      lowerCamelCase_       	=		self.image_processing_class(**self.image_processor_dict   )
      # create random numpy tensors
      lowerCamelCase_       	=		prepare_image_inputs(self.image_processor_tester			,		equal_resolution=__SCREAMING_SNAKE_CASE			,		numpify=__SCREAMING_SNAKE_CASE   )
      for image in image_inputs:
        self.assertIsInstance(__SCREAMING_SNAKE_CASE			,		np.ndarray   )

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      lowerCamelCase_       	=		image_processing(image_inputs[0]			,		return_tensors='pt'   ).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )






    def   UpperCamelCase			(			self	:				str   )	->						Optional[Any]:
      # Initialize image_processing
      lowerCamelCase_       	=		self.image_processing_class(**self.image_processor_dict   )
      # create random PyTorch tensors
      lowerCamelCase_       	=		prepare_image_inputs(self.image_processor_tester			,		equal_resolution=__SCREAMING_SNAKE_CASE			,		torchify=__SCREAMING_SNAKE_CASE   )
      for image in image_inputs:
        self.assertIsInstance(__SCREAMING_SNAKE_CASE			,		torch.Tensor   )

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      lowerCamelCase_       	=		image_processing(image_inputs[0]			,		return_tensors='pt'   ).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )












"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						list[list[int | float]]					) ->							int:
  lowerCamelCase_       	=		len(_lowerCamelCase					)
  lowerCamelCase_       	=		len(matrix[0]					)
  lowerCamelCase_       	=		min(_lowerCamelCase						,							_lowerCamelCase					)

  for row in range(_lowerCamelCase					):
    # Check if diagonal element is not zero
    if matrix[row][row] != 0:
      # Eliminate all the elements below the diagonal
      for col in range(row + 1						,							_lowerCamelCase					):
        lowerCamelCase_       	=		matrix[col][row] / matrix[row][row]
        for i in range(_lowerCamelCase						,							_lowerCamelCase					):
          matrix[col][i] -= multiplier * matrix[row][i]
    else:
      # Find a non-zero diagonal element to swap rows
      lowerCamelCase_       	=		True
      for i in range(row + 1						,							_lowerCamelCase					):
        if matrix[i][row] != 0:
          lowerCamelCase_	,      lowerCamelCase_       	=		matrix[i], matrix[row]
          lowerCamelCase_       	=		False
          break
      if reduce:
        rank -= 1
        for i in range(_lowerCamelCase					):
          lowerCamelCase_       	=		matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
      row -= 1

  return rank


if __name__ == "__main__":
  import doctest

  doctest.testmod()




